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BCon 147: special topics

Author

Insert your name here

Published

October 7, 2024

1 Project overiew

In this project, we will explore employee attrition and performance using the HR Analytics Employee Attrition & Performance dataset. The primary goal is to develop insights into the factors that contribute to employee attrition. By analyzing a range of factors, including demographic data, job satisfaction, work-life balance, and job role, we aim to help businesses identify key areas where they can improve employee retention.

2 Scenario

Imagine you are working as a data analyst for a mid-sized company that is experiencing high employee turnover, especially among high-performing employees. The company has been facing increased costs related to hiring and training new employees, and management is concerned about the negative impact on productivity and morale. The human resources (HR) team has collected historical employee data and now looks to you for actionable insights. They want to understand why employees are leaving and how to retain talent effectively.

Your task is to analyze the dataset and provide insights that will help HR prioritize retention strategies. These strategies could include interventions like revising compensation policies, improving job satisfaction, or focusing on work-life balance initiatives. The success of your analysis could lead to significant cost savings for the company and an increase in employee engagement and performance.

3 Understanding data source

The dataset used for this project provides information about employee demographics, performance metrics, and various satisfaction ratings. The dataset is particularly useful for exploring how factors such as job satisfaction, work-life balance, and training opportunities influence employee performance and attrition.

This dataset is well-suited for conducting in-depth analysis of employee performance and retention, enabling us to build predictive models that identify the key drivers of employee attrition. Additionally, we can assess the impact of various organizational factors, such as training and work-life balance, on both performance and retention outcomes.

## datatable function from DT package create an HTML widget display of the dataset
## install DT package if the package is not yet available in your R environment
readxl::read_excel("dataset/dataset-variable-description.xlsx") |> 
  DT::datatable()

4 Data wrangling and management

Libraries

Task: Load the necessary libraries

Before we start working on the dataset, we need to load the necessary libraries that will be used for data wrangling, analysis and visualization. Make sure to load the following libraries here. For packages to be installed, you can use the install.packages function. There are packages to be installed later on this project, so make sure to install them as needed and load them here.

# load all your libraries here
library(tidyverse)
library(readxl)
library(janitor)
library(tidytext)

4.1 Data importation

Task 4.1. Merging dataset
  • Import the two dataset Employee.csv and PerformanceRating.csv. Save the Employee.csv as employee_dta and PerformanceRating.csv as perf_rating_dta.

  • Merge the two dataset using the left_join function from dplyr. Use the EmployeeID variable as the varible to join by. You may read more information about the left_join function here.

  • Save the merged dataset as hr_perf_dta and display the dataset using the datatable function from DT package.

## import the two data here
employee_dta <- read_csv("dataset/Employee.csv")
perf_rating_dta <- read_csv("dataset/PerformanceRating.csv")

## merge employee_dta and perf_rating_dta using left_join function.
## save the merged dataset as hr_perf_dta
hr_perf_dta <- 
  employee_dta |> 
  left_join(perf_rating_dta, by = "EmployeeID") |> 
  mutate(bi_attrition = if_else(Attrition == "No", 0, 1))


## Use the datatable from DT package to display the merged dataset
DT::datatable(hr_perf_dta)

4.2 Data management

Task 4.2. Standardizing variable names
  • Using the clean_names function from janitor package, standardize the variable names by using the recommended naming of variables.

  • Save the renamed variables as hr_perf_dta to update the dataset.

## clean names using the janitor packages and save as hr_perf_dta
hr_perf_dta <- 
  hr_perf_dta |> 
  clean_names()

## display the renamed hr_perf_dta using datatable function
DT::datatable(hr_perf_dta)
Task 4.2. Recode data entries
  • Create a new variable cat_education wherein education is 1 = No formal education; 2 = High school; 3 = Bachelor; 4 = Masters; 5 = Doctorate. Use the case_when function to accomplish this task.

  • Similarly, create new variables cat_envi_sat, cat_job_sat, and cat_relation_sat for environment_satisfaction, job_satisfaction, and relationship_satisfaction, respectively. Re-code the values accordingly as 1 = Very dissatisfied; 2 = Dissatisfied; 3 = Neutral; 4 = Satisfied; and 5 = Very satisfied.

  • Create new variables cat_work_life_balance, cat_self_rating, cat_manager_rating for work_life_balance, self_rating, and manager_rating, respectively. Re-code accordingly as 1 = Unacceptable; 2 = Needs improvement; 3 = Meets expectation; 4 = Exceeds expectation; and 5 = Above and beyond.

  • Create a new variable bi_attrition by transforming attrition variable as a numeric variabe. Re-code accordingly as No = 0, and Yes = 1.

  • Save all the changes in the hr_perf_dta. Note that saving the changes with the same name will update the dataset with the new variables created.

## create cat_education

hr_perf_dta <- 
  hr_perf_dta |> 
  mutate(cat_education = case_when(
    education == 1 ~ "No formal education",
    education == 2 ~ "High school",
    education == 3 ~ "Bachelor",
    education == 4 ~ "Masters",
    education == 5 ~ "Doctorate"
  ))


## create cat_envi_sat,  cat_job_sat, and cat_relation_sat
hr_perf_dta <- 
  hr_perf_dta |> 
  mutate(cat_envi_sat = case_when(
    environment_satisfaction == 1 ~ "Very dissatisfied",
    environment_satisfaction == 2 ~ "Dissatisfied",
    environment_satisfaction == 3 ~ "Neutral",
    environment_satisfaction == 4 ~ "Satisfied",
    environment_satisfaction == 5 ~ "Very satisfied"
  )) |> 
  mutate(cat_job_sat = case_when(
    job_satisfaction == 1 ~ "Very dissatisfied",
    job_satisfaction == 2 ~ "Dissatisfied",
    job_satisfaction == 3 ~ "Neutral",
    job_satisfaction == 4 ~ "Satisfied",
    job_satisfaction == 5 ~ "Very satisfied"
  )) |> 
  mutate(cat_relation_sat = case_when(
    relationship_satisfaction == 1 ~ "Very dissatisfied",
    relationship_satisfaction == 2 ~ "Dissatisfied",
    relationship_satisfaction == 3 ~ "Neutral",
    relationship_satisfaction == 4 ~ "Satisfied",
    relationship_satisfaction == 5 ~ "Very satisfied"
  ))


## create cat_work_life_balance, cat_self_rating, and cat_manager_rating

hr_perf_dta <- 
  hr_perf_dta |> 
  mutate(cat_work_life_balance = case_when(
    work_life_balance == 1 ~ "Unacceptable",
    work_life_balance == 2 ~ "Needs improvement",
    work_life_balance == 3 ~ "Meets expectation",
    work_life_balance == 4 ~ "Exceeds expectation",
    work_life_balance == 5 ~ "Above and beyond"
  )) |> 
  mutate(cat_self_rating = case_when(
    self_rating == 1 ~ "Unacceptable",
    self_rating == 2 ~ "Needs improvement",
    self_rating == 3 ~ "Meets expectation",
    self_rating == 4 ~ "Exceeds expectation",
    self_rating == 5 ~ "Above and beyond"
  )) |> 
  mutate(cat_manager_rating = case_when(
    manager_rating == 1 ~ "Unacceptable",
    manager_rating == 2 ~ "Needs improvement",
    manager_rating == 3 ~ "Meets expectation",
    manager_rating == 4 ~ "Exceeds expectation",
    manager_rating == 5 ~ "Above and beyond"
  ))


## create bi_attrition

hr_perf_dta <- 
  hr_perf_dta |> 
  mutate(bi_attrition = if_else(attrition == "No", 0, 1))



## print the updated hr_perf_dta using datatable function
DT::datatable(hr_perf_dta)

5 Exploratory data analysis

5.1 Descriptive statistics of employee attrition

Task 5.1. Breakdown of attrition by key variables
  • Select the variables attrition, job_role, department, age, salary, job_satisfaction, and work_life_balance. Save as attrition_key_var_dta.

  • Compute and plot the attrition rate across job_role, department, and age, salary, job_satisfaction, and work_life_balance. To compute for the attrition rate, group the dataset by job role. Afterward, you can use the count function to get the frequency of attrition for each job role and then divide it by the total number of observations. Save the computation as pct_attrition. Do not forget to ungroup before storing the output. Store the output as attrition_rate_job_role.

  • Plot for the attrition rate across job_role has been done for you! Study each line of code. You have the freedom to customize your plot accordingly. Show your creativity!

## selecting attrition key variables and save as `attrition_key_var_dta`
attrition_key_var_dta <- 
  hr_perf_dta |> 
  select(attrition, 
         job_role,
         department, 
         age, 
         salary, 
         job_satisfaction, 
         work_life_balance
         )


## compute the attrition rate across job_role and save as attrition_rate_job_role

attrition_rate_job_role <-
  attrition_key_var_dta |> 
  group_by(job_role) |> 
  count(attrition) |> 
  mutate(pct_attrition = n / sum(n)) |> 
  ungroup()
  
## print attrition_rate_job_role
attrition_rate_job_role
# A tibble: 24 × 4
   job_role            attrition     n pct_attrition
   <chr>               <chr>     <int>         <dbl>
 1 Analytics Manager   No          185        0.869 
 2 Analytics Manager   Yes          28        0.131 
 3 Data Scientist      No          790        0.570 
 4 Data Scientist      Yes         597        0.430 
 5 Engineering Manager No          289        0.941 
 6 Engineering Manager Yes          18        0.0586
 7 HR Business Partner No           25        1     
 8 HR Executive        No           90        0.756 
 9 HR Executive        Yes          29        0.244 
10 HR Manager          No           17        1     
# ℹ 14 more rows
## Plot the attrition rate
attrition_rate_job_role |> 
  mutate(job_role = reorder_within(job_role, pct_attrition, attrition)) |> 
  ggplot(aes(pct_attrition, job_role, fill = attrition)) +
  geom_col(position = "dodge", width = 0.8) +
  scale_y_reordered() +
  facet_wrap(~ attrition, scales = "free_y", ncol = 1) +
  labs(x = "Attrition rate",
       y = "Job role")

5.2 Identifying attrition key drivers using correlation analysis

Task 5.2. Conduct a correlation analysis to identify key drivers
  • Conduct a correlation analysis of key variables: bi_attrition, salary, years_at_company, job_satisfaction, manager_rating, and work_life_balance. Use the cor() function to run the correlation analysis. Remove missing values using the na.omit() before running the correlation analysis. Save the output in hr_corr.

  • Use a correlation matrix or heatmap to visualize the relationship between these variables and attrition. You can use the GGally package and use the ggcorr function to visualize the correlation heatmap. You may explore this site for more information: ggcorr.

  • Discuss which factors seem most correlated with attrition and what that suggests aobut why employees are leaving.

## conduct correlation of key variables. 
hr_corr <- 
  hr_perf_dta |> 
  select(bi_attrition, salary, years_at_company, job_satisfaction, manager_rating, work_life_balance) |> 
  na.omit() |> 
  cor()

## print hr_corr 
hr_corr
                  bi_attrition       salary years_at_company job_satisfaction
bi_attrition       1.000000000 -0.211181478    -0.6896527798     0.0132368129
salary            -0.211181478  1.000000000     0.2206442116     0.0053054850
years_at_company  -0.689652780  0.220644212     1.0000000000     0.0008700583
job_satisfaction   0.013236813  0.005305485     0.0008700583     1.0000000000
manager_rating    -0.007654429 -0.001596736     0.0178656879    -0.0158205481
work_life_balance  0.003428836 -0.001517145     0.0079339508     0.0417242942
                  manager_rating work_life_balance
bi_attrition        -0.007654429       0.003428836
salary              -0.001596736      -0.001517145
years_at_company     0.017865688       0.007933951
job_satisfaction    -0.015820548       0.041724294
manager_rating       1.000000000       0.007996938
work_life_balance    0.007996938       1.000000000
## install GGally package and use ggcorr function to visualize the correlation
library(GGally)

ggcorr(hr_corr, label = TRUE)

Discussion:

Provide your discussion here.

5.3 Predictive modeling for attrition

Task 5.3. Predictive modeling for attrition
  • Create a logistic regression model to predict employee attrition using the following variables: salary, years_at_company, job_satisfaction, manager_rating, and work_life_balance. Save the model as hr_attrition_glm_model. Print the summary of the model using the summary function.

  • Install the sjPlot package and use the tab_model function to display the summary of the model. You may read the documentation here on how to customize your model summary.

  • Also, use the plot_model function to visualize the model coefficients. You may read the documentation here on how to customize your model visualization.

  • Discuss the results of the logistic regression model and what they suggest about the factors that contribute to employee attrition.

## run a logistic regression model to predict employee attrition
## save the model as hr_attrition_glm_model
hr_attrition_glm_model <- 
  glm(bi_attrition ~ salary + years_at_company + job_satisfaction + manager_rating + work_life_balance, 
      data = hr_perf_dta, 
      family = "binomial")

## print the summary of the model using the summary function
summary(hr_attrition_glm_model)

Call:
glm(formula = bi_attrition ~ salary + years_at_company + job_satisfaction + 
    manager_rating + work_life_balance, family = "binomial", 
    data = hr_perf_dta)

Coefficients:
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)        2.571e+00  2.173e-01  11.831   <2e-16 ***
salary            -3.633e-06  4.086e-07  -8.893   <2e-16 ***
years_at_company  -6.333e-01  1.476e-02 -42.919   <2e-16 ***
job_satisfaction   3.470e-02  3.186e-02   1.089    0.276    
manager_rating     5.071e-03  3.810e-02   0.133    0.894    
work_life_balance  2.587e-02  3.198e-02   0.809    0.419    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 8574.5  on 6708  degrees of freedom
Residual deviance: 4781.6  on 6703  degrees of freedom
  (190 observations deleted due to missingness)
AIC: 4793.6

Number of Fisher Scoring iterations: 5
## install sjPlot package and use tab_model function to display the summary of the model
library(sjPlot)

tab_model(hr_attrition_glm_model)
  bi attrition
Predictors Odds Ratios CI p
(Intercept) 13.08 8.56 – 20.07 <0.001
salary 1.00 1.00 – 1.00 <0.001
years at company 0.53 0.52 – 0.55 <0.001
job satisfaction 1.04 0.97 – 1.10 0.276
manager rating 1.01 0.93 – 1.08 0.894
work life balance 1.03 0.96 – 1.09 0.419
Observations 6709
R2 Tjur 0.502
## use plot_model function to visualize the model coefficients
plot_model(hr_attrition_glm_model)

Discussion:

Provide your discussion here.

5.4 Analysis of compensation and turnover

Task 5.4.1. Analyzing compensation and turnover
  • Compare the average monthly income of employees who left the company (bi_attrition = 1) and those who stayed (bi_attrition = 0). Use the t.test function to conduct a t-test and determine if there is a significant difference in average monthly income between the two groups. Save the results in a variable called attrition_ttest_results.

  • Install the report package and use the report function to generate a report of the t-test results.

  • Install the ggstatsplot package and use the ggbetweenstats function to visualize the distribution of monthly income for employees who left and those who stayed. Make sure to map the bi_attrition variable to the x argument and the salary variable to the y argument.

  • Visualize the salary variable for employees who left and those who stayed using geom_histogram with geom_freqpoly. Make sure to facet the plot by the bi_attrition variable and apply alpha on the histogram plot.

  • Provide recommendations on whether revising compensation policies could be an effective retention strategy.

## compare the average monthly income of employees who left and those who stayed
attrition_ttest_results <- 
  t.test(salary ~ bi_attrition, data = hr_perf_dta)


## print the results of the t-test
attrition_ttest_results

    Welch Two Sample t-test

data:  salary by bi_attrition
t = 18.869, df = 5524.2, p-value < 2.2e-16
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 38577.82 47523.18
sample estimates:
mean in group 0 mean in group 1 
      125007.26        81956.76 
## install the report package and use the report function to generate a report of the t-test results
library(report)

report(attrition_ttest_results)
Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference of salary by bi_attrition
(mean in group 0 = 1.25e+05, mean in group 1 = 81956.76) suggests that the
effect is positive, statistically significant, and medium (difference =
43050.50, 95% CI [38577.82, 47523.18], t(5524.24) = 18.87, p < .001; Cohen's d
= 0.51, 95% CI [0.45, 0.56])
# install ggstatsplot package and use ggbetweenstats function to visualize the distribution of monthly income for employees who left and those who stayed
library(ggstatsplot)

ggbetweenstats(data = hr_perf_dta, x = bi_attrition, y = salary)

# create histogram and frequency polygon of salary for employees who left and those who stayed

hr_perf_dta |> 
  ggplot(aes(x = salary)) +
  geom_histogram(alpha = 0.3) +
  geom_freqpoly() +
  facet_wrap(~ bi_attrition) +
  labs(x = "Attrition",
       y = "Monthly income")

Discussion:

Provide your discussion here.

5.5 Employee satisfaction and performance analysis

Task 5.5. Analyzing employee satisfaction and performance
  • Analyze the average performance ratings (both ManagerRating and SelfRating) of employees who left vs. those who stayed. Use the group_by and count functions to calculate the average performance ratings for each group.

  • Visualize the distribution of SelfRating for employees who left and those who stayed using a bar plot. Use the ggplot function to create the plot and map the SelfRating variable to the x argument and the bi_attrition variable to the fill argument.

  • Similarly, visualize the distribution of ManagerRating for employees who left and those who stayed using a bar plot. Make sure to map the ManagerRating variable to the x argument and the bi_attrition variable to the fill argument.

  • Create a boxplot of salary by job_satisfaction and bi_attrition to analyze the relationship between salary, job satisfaction, and attrition. Use the geom_boxplot function to create the plot and map the salary variable to the x argument, the job_satisfaction variable to the y argument, and the bi_attrition variable to the fill argument. You need to transform the job_satisfaction and bi_attrition variables into factors before creating the plot or within the ggplot function.

  • Discuss the results of the analysis and provide recommendations for HR interventions based on the findings.

# Analyze the average performance ratings (both ManagerRating and SelfRating) of employees who left vs. those who stayed.

hr_perf_dta |> 
  na.omit() %>% 
  group_by(bi_attrition) |> 
  summarise(avg_manager_rating = mean(manager_rating, na.rm = TRUE),
            avg_self_rating = mean(self_rating, na.rm = TRUE))
# A tibble: 2 × 3
  bi_attrition avg_manager_rating avg_self_rating
         <dbl>              <dbl>           <dbl>
1            0               3.48            3.98
2            1               3.46            3.99
# Visualize the distribution of SelfRating for employees who left and those who stayed using a bar plot.

hr_perf_dta |> 
  na.omit() %>% 
  ggplot(aes(x = self_rating, fill = factor(bi_attrition))) +
  geom_bar(position = "dodge") +
  labs(x = "Self Rating",
       y = "Count",
       fill = "Attrition")

# Visualize the distribution of ManagerRating for employees who left and those who stayed using a bar plot.
hr_perf_dta |> 
  na.omit() %>%
  ggplot(aes(x = manager_rating, fill = factor(bi_attrition))) +
  geom_bar(position = "dodge") +
  labs(x = "Manager Rating",
       y = "Count",
       fill = "Attrition")

# create a boxplot of salary by job_satisfaction and bi_attrition to analyze the relationship between salary, job satisfaction, and attrition.

hr_perf_dta |> 
  na.omit() %>%
  ggplot(aes(y = factor(job_satisfaction), x = salary, fill = factor(bi_attrition))) +
  geom_boxplot() +
  labs(x = "Job Satisfaction",
       y = "Salary",
       fill = "Attrition")

Discussion:

Provide your discussion here.

5.6 Work-life balance and retention strategies

Task 5.6. Analyzing work-life balance and retention strategies

At this point, you are already well aware of the dataset and the possible factors that contribute to employee attrition. Using your R skills, accomplish the following tasks:

  • Analyze the distribution of WorkLifeBalance ratings for employees who left versus those who stayed.

  • Use visualizations to show the differences.

  • Assess whether employees with poor work-life balance are more likely to leave.

You have the freedom how you will accomplish this task. Be creative and provide insights that will help HR develop effective retention strategies.

5.7 Recommendations for HR interventions

Task 5.7.1. Recommendations for HR interventions

Based on the analysis conducted, provide recommendations for HR interventions that could help reduce employee attrition and improve overall employee satisfaction and performance. You may use the following question as guide for your recommendations and discussions.

  • What are the key factors contributing to employee attrition in the company?

  • Which factors are most strongly correlated with attrition?

  • What strategies could be implemented to improve employee retention and satisfaction?

  • How can HR leverage the insights from the analysis to develop effective retention strategies?

  • What are the potential benefits of implementing these strategies for the company?